Pictorial images are often classified by the particular event, subject or the like for convenience of retrieving, reviewing, and albuming of the images. Typically, this has been achieved by manually segmenting the images, or by the below-described automated method. The automated method includes grouping by color, shape or texture of the images for partitioning the images into groups of similar image characteristics.
Although the presently known and utilized methods for partitioning images are satisfactory, there are drawbacks. The manual classification is obviously time consuming, and the automated process, although theoretically classifying the images into events, is susceptible to miss-classification due to the inherent inaccuracies involved with classification by color, shape or texture.
Consequently, a need exists for overcoming the above-described drawbacks. However, in many cases, chronological data such as date and time is not available. The need therefore is to develop an intelligent algorithm to automatically classify and sort images from sets/rolls of pictures into separate events based on image content only. Moreover, such an algorithm should be fully automatic, work on all consumer pictures of different sizes without needing date or time information, and be adaptable to different parameters.